Amazon Sagemaker
Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. With Amazon SageMaker, all the barriers and complexity that typically slow down developers who want to use machine learning are removed. The service includes models that can be used together or independently to build, train, and deploy your machine learning models.
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Medical LLM - Medium Free trial
By:
Latest Version:
5.4.5
Use for chat, RAG, medical summarization, open-book question answering with context of up to 32K tokens.
Product Overview
Trained on diverse medical texts, this model excels in summarizing, answering complex clinical questions, and transforming detailed clinical notes, patient encounters, and various medical reports into concise, digestible summaries. The summarization feature boosts efficiency while preserving critical details, supporting optimal patient care. Its question-answering capability ensures accurate, context-specific responses to both open and closed medical queries, further enhancing decision-making. For physicians, this tool offers a quick grasp of a patient’s medical history, aiding timely and informed decisions. Instead of sifting through extensive documentation, doctors can rely on these summaries to understand a patient’s journey, condition, and treatment protocols swiftly. Optimized for Retrieval-Augmented Generation (RAG), the model can be used in combination with healthcare databases, EHR, and scientific literature repositories (like PubMed) to enhance response quality.
Key Data
Version
Type
Model Package
Highlights
Real-Time Inference
- Instance Type: ml.g6.48xlarge
- Maximum supported context length for this instance type: 8k
- Tokens per Second during real-time inference:
- Summarization: up to 10 tokens per second
- QA: up to 12 tokens per second
Batch Transform
- Instance Type: ml.g5.48xlarge
- Maximum supported context length for this instance type: 8k
- Tokens per Second:
- Summarization: up to 20 tokens per second
- QA: up to 50 tokens per second
Benchmarking Results:
- Achieves 86.31% average on OpenMed benchmarks, surpassing GPT-4 (82.85%) and Med-PaLM-2 (84.08%)
- Performance in medical genetics: 95%; performance in professional medicine: 94.85%
- Clinical knowledge comprehension 89.81% and college biology mastery 93.75%
- Achieves 58.9% average on standard LLM benchmarks
- Balance of specialized medical knowledge and broad language understanding, demonstrated by 70.93% on GPT4All benchmark
- Achieves 75.54% performance in medical MCQAs and 79.4% on PubMedQA
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Pricing Information
Use this tool to estimate the software and infrastructure costs based your configuration choices. Your usage and costs might be different from this estimate. They will be reflected on your monthly AWS billing reports.
Contact us to request contract pricing for this product.
Estimating your costs
Choose your region and launch option to see the pricing details. Then, modify the estimated price by choosing different instance types.
Version
Region
Software Pricing
Model Realtime Inference$19.96/hr
running on ml.g6.48xlarge
Model Batch Transform$19.96/hr
running on ml.g5.48xlarge
Infrastructure PricingWith Amazon SageMaker, you pay only for what you use. Training and inference is billed by the second, with no minimum fees and no upfront commitments. Pricing within Amazon SageMaker is broken down by on-demand ML instances, ML storage, and fees for data processing in notebooks and inference instances.
Learn more about SageMaker pricing
With Amazon SageMaker, you pay only for what you use. Training and inference is billed by the second, with no minimum fees and no upfront commitments. Pricing within Amazon SageMaker is broken down by on-demand ML instances, ML storage, and fees for data processing in notebooks and inference instances.
Learn more about SageMaker pricing
SageMaker Realtime Inference$16.688/host/hr
running on ml.g6.48xlarge
SageMaker Batch Transform$20.36/host/hr
running on ml.g5.48xlarge
About Free trial
Try this product for 15 days. There will be no software charges, but AWS infrastructure charges still apply. Free Trials will automatically convert to a paid subscription upon expiration.
Model Realtime Inference
For model deployment as Real-time endpoint in Amazon SageMaker, the software is priced based on hourly pricing that can vary by instance type. Additional infrastructure cost, taxes or fees may apply.InstanceType | Realtime Inference/hr | |
---|---|---|
ml.g6.48xlarge Vendor Recommended | $19.96 |
Usage Information
Model input and output details
Input
Summary
For a complete description of the input format and parameters go here
Input MIME type
application/json, application/jsonlinesSample input data
Output
Summary
The output is a JSON object or a set of JSON Lines objects that contain the generated text(s)
JSON Format { "response": [ "model response for input 1", "model response for input 2", ... ] } JSON Lines (JSONL) Format {"response": "model response for input 1"} {"response": "model response for input 2"}
The JSON Lines format consists of separate JSON objects, where each object represents a model response for the respective input.
Output MIME type
application/json, application/jsonlinesSample output data
Sample notebook
End User License Agreement
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Support Information
Medical LLM - Medium
For any assistance, please reach out to support@johnsnowlabs.com.
AWS Infrastructure
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